Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication

A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and sh...

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Auteurs principaux: Yufei Liu, Feng Zhou, Gang Qiao, Yunjiang Zhao, Guang Yang, Xinyu Liu, Yinheng Lu
Format: article
Langue:EN
Publié: MDPI AG 2021
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Accès en ligne:https://doaj.org/article/c96b736ec43c41df9a58407429dbf879
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spelling oai:doaj.org-article:c96b736ec43c41df9a58407429dbf8792021-11-25T18:04:49ZDeep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication10.3390/jmse91112522077-1312https://doaj.org/article/c96b736ec43c41df9a58407429dbf8792021-11-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1252https://doaj.org/toc/2077-1312A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10<sup>−3</sup> can be obtained at a signal-to-noise ratio of −8 dB.Yufei LiuFeng ZhouGang QiaoYunjiang ZhaoGuang YangXinyu LiuYinheng LuMDPI AGarticlecyclic shift keying spread spectrumlow signal-to-noise ratiomultipath effectsneural network modellong- and short-term memoryNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1252, p 1252 (2021)
institution DOAJ
collection DOAJ
language EN
topic cyclic shift keying spread spectrum
low signal-to-noise ratio
multipath effects
neural network model
long- and short-term memory
Naval architecture. Shipbuilding. Marine engineering
VM1-989
Oceanography
GC1-1581
spellingShingle cyclic shift keying spread spectrum
low signal-to-noise ratio
multipath effects
neural network model
long- and short-term memory
Naval architecture. Shipbuilding. Marine engineering
VM1-989
Oceanography
GC1-1581
Yufei Liu
Feng Zhou
Gang Qiao
Yunjiang Zhao
Guang Yang
Xinyu Liu
Yinheng Lu
Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
description A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10<sup>−3</sup> can be obtained at a signal-to-noise ratio of −8 dB.
format article
author Yufei Liu
Feng Zhou
Gang Qiao
Yunjiang Zhao
Guang Yang
Xinyu Liu
Yinheng Lu
author_facet Yufei Liu
Feng Zhou
Gang Qiao
Yunjiang Zhao
Guang Yang
Xinyu Liu
Yinheng Lu
author_sort Yufei Liu
title Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
title_short Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
title_full Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
title_fullStr Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
title_full_unstemmed Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
title_sort deep learning-based cyclic shift keying spread spectrum underwater acoustic communication
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/c96b736ec43c41df9a58407429dbf879
work_keys_str_mv AT yufeiliu deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication
AT fengzhou deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication
AT gangqiao deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication
AT yunjiangzhao deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication
AT guangyang deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication
AT xinyuliu deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication
AT yinhenglu deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication
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